Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

547
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
547
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

14.9K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
14.9K
Variation01:19

Variation

8.2K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.2K
What is Variation?01:14

What is Variation?

18.8K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.8K
Variance01:15

Variance

12.8K
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
12.8K
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

22
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
22

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same authorSame journal

Using reactive links to propagate changes across engineering models.

Software and systems modeling·2025
Same author

Model-driven engineering of safety and security software systems: A systematic mapping study and future research directions.

Journal of software (Malden, MA)·2024
Same author

Generating repairs for inconsistent models.

Software and systems modeling·2023
Same author

Consistent change propagation within models.

Software and systems modeling·2021
Same journal

Formalising privacy regulations with bigraphs.

Software and systems modeling·2026
Same journal

The MDENet education platform: zero-install directed activities for learning MDE.

Software and systems modeling·2026
Same journal

Diagrammatic physical robot models.

Software and systems modeling·2025
Same journal

Extract, model, refine: improved modelling of program verification tools through data enrichment.

Software and systems modeling·2025
Same journal

What makes life for process mining analysts difficult? A reflection of challenges.

Software and systems modeling·2024
See all related articles

Related Experiment Video

Updated: Feb 20, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K

Variability extraction and modeling for product variants.

Lukas Linsbauer1, Roberto Erick Lopez-Herrejon1, Alexander Egyed1

  • 1Institute for Software Systems Engineering, Johannes Kepler University, Linz, Austria.

Software and Systems Modeling
|October 27, 2017
PubMed
Summary
This summary is machine-generated.

Developing software variants is complex. This study presents a method to automatically extract variability information, improving efficiency and accuracy for software product lines and other reuse approaches.

Keywords:
DependencyFeatureProduct variantTraceVariability

More Related Videos

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.3K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.2K

Related Experiment Videos

Last Updated: Feb 20, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.3K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.2K

Area of Science:

  • Software Engineering
  • Computer Science

Background:

  • Diverse customer requirements necessitate tailored software, often leading to complex product variants.
  • Current methods for managing software variants, like clone-and-own, are inefficient and error-prone.
  • Systematic approaches require detailed information on variant features and their relationships.

Purpose of the Study:

  • To present a disciplined and systematic approach for extracting variability information from software product variants.
  • To identify traces from features and feature interactions to their implementation artifacts.
  • To compute dependencies for improved software variant management.

Main Methods:

  • Developed an approach to automatically extract variability information from software product variants.
  • Implemented feature tracing to identify implementation artifacts.
  • Computed feature and artifact dependencies.

Main Results:

  • The approach successfully extracted variability information across six case studies.
  • Extracted information was consistent with existing variability models and product variants.
  • Demonstrated improved efficiency and accuracy in managing software diversity.

Conclusions:

  • The proposed variability extraction approach enhances the management of software product lines and other reuse scenarios.
  • Automated extraction of variability information reduces errors and improves efficiency.
  • This method provides crucial data for disciplined software variant development and maintenance.